Radiology AI training and assessment-challenges, innovations, and a path forward.

Area of Science:

• Medical Imaging and Diagnostics
• Artificial Intelligence in Healthcare
• Medical Education Technology

Background:

• Artificial intelligence (AI) is increasingly prevalent in medical devices, particularly in imaging.
• Radiology training programs face challenges in adapting curricula to include AI competencies.
• Current medical curricula show inconsistent integration of AI, with a lack of competency assessments.

Purpose of the Study:

• To review the current state of AI in UK radiology training curricula.
• To explore the impact of AI on competency evaluations and professional assessments.
• To examine AI-driven educational resources for enhancing radiology training.

Main Methods:

• Literature review of AI in UK medical training curricula, focusing on radiology.
• Analysis of AI's potential impact on assessments like FRCR, ARCP, and on-call evaluations.
• Exploration of AI-assisted training platforms and educational resources.

Main Results:

• AI adoption is accelerating, with most AI medical devices related to imaging.
• AI integration into radiology curricula and assessments is inconsistent.
• AI-powered educational tools offer potential for enhanced learning experiences.

Conclusions:

• Radiology training must evolve to include AI literacy and competency assessment.
• Structured national AI teaching and AI-focused assessments are proposed.
• Equipping radiologists with AI skills is crucial for future healthcare transformation.